1. Field of the Invention
The present invention generally relates to a system and method for constructing a social network, and more particularly, the present invention relates to a system, method, and framework for constructing a social network from multiple, disparate, heterogeneous data sources, in which at least one of the data sources includes a user generated data source.
2. Description of the Related Art
For purposes of the present application, the term “social network” generally means a social structure made of nodes which are generally individuals or organizations, and edges or links between them.
For purposes of the present application, the term “social computing”, or social network technology, generally means the use of social software. Social computing represents a growing trend of tools supporting social interaction and communication. For example, social computing can include email, instant messaging, blogs, wikis, auctions, web interactive communication or research, online social networking websites, etc.
A social network is a map of the relationships between individuals, indicating the ways in which they are connected through various social familiarities ranging from casual acquaintance to close familial bonds. The term was first coined in 1954 by J. A. Barnes (in: Class and Committees in a Norwegian Island Parish, “Human Relations”). Social network analysis (SNA) (also sometimes called network theory) has emerged as a key technique in modern sociology, anthropology, social psychology and organizational studies.
Research in a number of academic fields has demonstrated that social networks operate on many levels, from families up to the level of nations, and play a critical role in determining the way problems are solved, organizations are run, information is shared, and the degree to which individuals succeed in achieving their goals.
Social networking also refers to a category of Internet applications to help connect friends, business partners, or other individuals together using a variety of tools. These applications, known as online social networks are becoming increasingly popular.
Generally, social network theory views social relationships in terms of nodes and ties (or ties). Nodes are the individual actors within the networks, and linkages are the relationships between the actors.
There can be many kinds of linkages between the nodes. In its most simple form, a social network is a map of all of the relevant linkages between the nodes being studied. The network can also be used to determine the social capital of individual actors. These concepts are often displayed in a social network diagram, where nodes are the points and linkages are the lines.
The shape of the social network helps determine a network's usefulness to its individuals. Smaller, tighter networks can be less useful to their members than networks with lots of loose connections (weak ties) to individuals outside the main network. More “open” networks, with many weak ties and social connections, are more likely to introduce new ideas and opportunities to their members than closed networks with many redundant ties. In other words, a group of friends who only do things with each other already share the same knowledge and opportunities. A group of individuals with connections to other social worlds is likely to have access to a wider range of information. It is better for individual success to have connections to a variety of networks rather than many connections within a single network. Similarly, individuals can exercise influence or act as brokers within their social networks by bridging two networks that are not directly linked (called filling social holes).
The power of social network theory stems from its difference from traditional sociological studies, which assume that it is the attributes of individual actors that matter. Social network theory produces an alternate view, where the attributes of individuals are less important than their relationships and ties with other actors within the network. This approach has turned out to be useful for explaining many real-world phenomena, but leaves less room for individual agency, and the ability for individuals to influence their success, since so much of it rests within the structure of their network.
Social networks have also been used to examine how companies interact with each other, characterizing the many informal connections that link executives together, as well as associations and connections between individual employees at different companies. These networks provide ways for companies to gather information, deter competition, and even collude in setting prices or policies.
Power within organizations, for example, generally has been found to come more from the degree to which an individual within a network is at the center of many relationships than actual job title. Social networks also play a key role in hiring, in business success for firms, and in job performance.
Social networking websites (e.g., online social networks) have become widely used in virtual communities. In these communities, an initial set of founders sends out messages inviting members of their own personal networks to join the site. New members repeat the process, growing the total number of members and links in the network. Sites then offer features such as automatic address book updates, viewable profiles, the ability to form new links through “introduction services,” and other forms of online social connections. Social networks can also be organized around business connections.
Blended networking is an approach to social networking that combines both offline elements (face-to-face events) and online elements. The newest social networks on the Internet are becoming more focused on niches.
The following are some terms which generally are used in describing social networks.
The term “betweenness” generally means the degree an individual lies between other individuals in the network; the extent to which a node is directly connected only to those other nodes that are not directly connected to each other; an intermediary; liaisons; bridges. Therefore, “betweenness” generally means the number of people who a person is connected to indirectly through their direct links.
The term “closeness” generally means the degree an individual is near all other individuals in a network (directly or indirectly) and reflects the ability to access information through the “grapevine” of network members. Thus, closeness is the inverse of the sum of the shortest distances between each individual and every other person in the network.
The term “degree” generally means the count of the number of linkages or ties to other actors in the network.
The term “Eigenvector Centrality” generally is a measure of the importance of a node in a network. It generally assigns relative scores to all nodes in the network based on the principle that connections to nodes having a high score contribute more to the score of the node in question.
The term “clustering coefficient” generally means a measure of the likelihood that two associates of a node are associates themselves. A higher clustering coefficient indicates a greater ‘cliquishness’.
The term “cohesion” generally means the degree to which actors are connected directly to each other by cohesive bonds. Groups generally are identified as ‘cliques’ if every actor is directly tied to every other actor, or ‘social circles’ if there is less stringency of direct contact.
The term “individual-level density” generally means the degree to which a respondents linkages know one another, or the proportion of linkages among an individual's nominees. The term “network or global-level density” is the proportion of linkages in a network relative to the total number possible (sparse versus dense networks).
The term “group degree centralization” generally means a measure of group dispersion or how network links focus on a specific node or nodes.
The term “radiality” generally means the degree an individual's network reaches out into the network and provides novel information and influence
The term “reach” generally means the degree any member of a network can reach other members of the network.
The term “structural equivalence” generally means the extent to which actors have a common set of linkages to other actors in the system. The actors don't need to have any linkages to each other to be structurally equivalent.
The term “static holes” generally means structural holes that can be strategically filled by connecting one or more links to link together other points. Linked to ideas of social capital: if you link to two people who are not linked you can control their communication.
Conventional methods generally rely on only one, or only homogeneous sources of data to construct the social network. The problem is that the analysis performed based on the derived social network is limited.
Furthermore, the conventional methods do not consider more than one source of user generated information, nor do they consider user generated sources in combination with non-user generated sources.
Conventional methods generally describe construction of social networks with multiple kinds of edges, reflecting different kinds of relationships. However, the conventional methods use only one data source used to construct the edges.
Other conventional methods use SNA to build knowledge maps, which are constructs within the area of knowledge management. Such conventional methods borrow from the standard practice of SNA (and other disciplines), but do not suggest or extend construction methods, according to the exemplary aspects of the present invention.